U.S. Pat. No. 6,144,989, incorporated by reference herein, describes an adaptive agent oriented software architecture (AAOSA), in which an agent network is developed for the purpose of interpreting user input as commands and inquiries for a back-end application, such as an audiovisual system or a financial reporting system. User input is provided to the natural language interpreter in a predefined format, such as a sequence of tokens, often in the form of text words and other indicators. The interpreter parses the input and attempts to discern from it the user's intent relative to the back-end application. The interpreter sometimes needs to interact with the user in order to make an accurate interpretation, and it can do so by outputting to the user an inquiry or request for clarification. In addition, the back-end application also needs to be able to provide output to the user, such as responses to the user's commands, or other output initiated by the application. AAOSA is one example of a natural language interpreter; another example is Nuance Communications' Nuance Version 8 (“Say Anything”) product, described in Nuance Communications, “Developing Flexible Say Anything Grammars, Nuance Speech University Student Guide” (2001), incorporated herein by reference.
Natural language interpreters have become very good at interpreting user's intent in many situations. Most systems rely on some sort of word-spotting algorithm that has been pre-defined by a programmer for the particular back-end application. In some situations, however, the language used by the user might not have been anticipated by the programmer, sometimes resulting either in commands that are either not recognized or recognized incorrectly. If they are not recognized, then the user might experience no response from the system, and if they are recognized incorrectly, then the system might command the back-end application to perform a function different from the user's intent. U.S. Pat. No. 6,144,989, incorporated above, provides some techniques for learning from contradiction resolution and from user dissatisfaction with the results of an interpretation, but additional mechanisms are needed.
Roughly described, the invention addresses the above problems through the formalized use of synonyms and suggestions. Synonyms are learned by the system using an explicit learning mechanism, and suggestions are learned using a form of implicit learning. In addition, many of the mechanisms for implementing suggestions can also be used to implement an adaptive, context-based “push” functionality (sometimes referred to herein as proposals), in which the suggestions are programmed by someone other than the user. In addition, a novel statistics based reinforcement algorithm can be used to improve the accurate selection of suggestions and proposals.